Intelligent modeling and control of automation

ABSTRACT

A system and method for advanced device specific knowledge based modeling as well as intelligent control to yield high performance, low cost automation for optoelectronic design, packaging and assembly. The control loop design is based on knowledge based model predictive control. A knowledge model, specific to the assembled package&#39;s characteristics, is used to set the initial “feed-forward” conditions of an automation system. In addition to this feed-forward model for setting the initial set point, the controller is designed with feedback components, along with the inclusion of a built in sensor. This system and method increases the efficiency of the automation process and the number of assembly steps can be greatly reduced. A method for the design, assembly and packaging of optoelectronic devices is also described.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/576,159, filed on Jun. 26, 2006, now U.S. Pat. No. 7,526,356currently pending, which, in turn, is a 371 continuation ofInternational patent application number PCT/US2004/033855, filed on Oct.15, 2004, now expired, which, in turn, is a nonprovisional of U.S.provisional patent application No. 60/512,567, filed on Oct. 17, 2003.

BACKGROUND OF INVENTION

1. Field of Invention

This invention relates to the automation of the packaging and assemblyof optoelectronics. Specifically, the present invention relates to theprovision of intelligent control and system level modeling in order toobtain high performance, low cost automation of assembly and packaging.

2. Description of the Related Technology

The current trend in optical microsystem design is to exploit advanceddevices and new system architectures to achieve greater systemperformance, such as higher data rates or brighter displays.Advancements in the optics field may have driven up the demand forcomplicated devices, however the packaging and assembly of thesecomplicated devices has not increased in sophistication. As aconsequence the current methods of packaging and assemblingoptoelectronics do not produce the most favorable results.

Examples of new devices increasing optical capacity are numerous.Research is being performed in micro-electrical-mechanical systems(MEMS), in which micro-machined mirrors steer an optical signal througha switching network. Next generation systems, supporting terabit/seccommunication are being designed with thin film electro-opticmodulators, low-loss hetero-structure waveguides and photonic integratedcircuits, and high efficiency, edge-emitting, multi-wavelength quantumdot laser arrays. Other nanostructures are being used in WDM systems foroptical signal processing, polarization control of VCSEL lasers,all-optical buffers, and micro-resonators. Beyond the telecommunicationsfield, there have been advances in devices for displays and sensors.These include, holographic polymer dispersed liquid crystals, photoniccrystals, and nano-tubes.

Although there has been much advancement in the field of complex opticaldevices, there has been little to no advancement in the assembly orpackaging of these products. However, to push towards the theoreticallimits of optical Microsystems, accurate alignment and packaging ofmulti-domain systems is required. Packaging is a challenging problem, assystems are typically manually aligned. This technique is laborintensive, slow, and can lead to a poor performance of the opticalsystem. Even with recent progress in the development of devices andMicrosystems, the packaging and assembly of these systems remains as apossible critical limiting factor to their commercial success.

Automation is the key to high volume, low cost, and high consistencymanufacturing, while ensuring performance, reliability and quality.There is a growing interest in the development of automation techniquesfor photonic alignment and packaging, as the optical microsystemindustry desires the benefits of automation experienced by, for example,the semiconductor industry. However, the photonic community cannotsimply use the same automation processes as the semiconductor industry.The equipment is not optimized for optoelectronic packaging automationsince the optical and geometric axes of these optical Microsystems areoften not aligned with one another. This points out the fundamentaldifference between electrical, or semiconductor automation, and opticalautomation. In the electrical domain, a good attachment occurs betweentwo components when they physically touch and solder flows between them.However, in the optical domain, not only is a good connection needed, anexact orientation alignment is required. As a result, packaging costscurrently account for 60-80% of the entire photonic component cost.

The current automation technique used has many limitations. First, ifthe optical wavefront is not a symmetric uni-mode function, the controlalgorithm can get “caught” at local power maximums instead of the globalmaximum of the entire wavefront. This error can yield a dramatic loss inpower efficiency, SNR, and BER for the assembled product. Therefore, asthe complexity of the optical wavefront increases, possibly with theaddition of complex devices such as MEMS and diffractive opticalelements (DOE), the current technique of alignment might not yieldmaximum system performance.

Secondly, since multi-space searches are employed with a gradient ascentalgorithm, the convergence time of the alignment equipment will dependon factors such as the control resolution and processing power. Apackage with multiple degrees of freedom may result in a delayedassembly line, since the gradient ascent algorithm for multiple axes isvery slow and sometimes non-converging. This increases the cost of theautomation process. Lastly, current servos and control (PID) loopsdeployed for semiconductor equipment do not employ process knowledgebase data in the loop.

Most of the existing photonic automation systems couple laser diodes tofiber, fiber to fiber, or waveguide (on an integrated circuit) to afiber. The state-of-the-art technology is based on industrial andsemiconductor automation, robotics, motion control, sensor technology,and existing capital equipment. For uni-mode optical signals, such asGaussian shaped beams emitted from laser sources, waveguides, andfibers, photonic automation is advancing. However, to date, nosignificant defined standard has been developed to implement automationfor general optical systems. Therefore, the majority of production linesfor photonic systems are still only poorly automated.

Currently, photonic alignment research is performed in academicinstitutions by examining how packaging and alignment can be designed inthe system substrate through micromachining. In addition, some leadingautomation and optical component companies have realized the importanceof automation for photonic systems. The control loop implemented bythese industries is described in and seen in FIG. 1.

The technique in FIG. 1 is based on a combination of visual inspectionand maximizing power alignments. This work has shown promise for thesupport of optical automation for simple uni-modal power distributions,as the Proportional Integral Derivative (PID) loops converge to a singlemode. The loop 100 in FIG. 1 is called the servo-feedback loop. Theservo-feedback loop performs a gradient ascent 108 on the measuredoptical power by comparing consecutive power readings P_(k) and P_(k−1)112 at configurations x_(k) and x_(k−1). A gradient,(P_(k)−P_(k−1))/(x_(k)−x_(k−1)), is formed which guides the axis motionto the next configuration, x_(k+1):x _(k+1) =x _(k)+η((P _(k) −P _(k−1))/(x _(k) −x _(k−1)))where, η is the gradient accent coefficient, which is the resolution ofthe step.

Currently, the control loop is initiated to a set point (x₀) by a visionsystem 102. Key shapes of the fiber or waveguide are searched for in thefield of vision of a CCD camera focused at the alignment and attachmentpoint. From these searches, the automation software “visualizes” thedesired link, and initializes the control motors with a determined setpoint via the initialization loop 104. After determining the vision setpoint, the alignment is fine-tuned by the local gradient ascent searchto a local power maximum, as described in FIG. 1. Each axis of motion isindependently controlled, and typically, the number of controlled axesis quite small. To obtain the required power measurement, a laser isused to excite the system and a power meter is attached to the outputfiber, this can be seen in step 106. In the event that the system is notbeing aligned correctly the system stops and the alignment is fixed instep 110. In efforts to decrease the amount of time to determine thepeak power mode, efficient positioning algorithms have been implemented,based on the assumption that the power distribution will always be auni-mode (Gaussian) shape. The algorithm picks three initial points andmeasures the power at each. From these results, the algorithm determinesthree new points based on a Gaussian distribution, and continues thisprocess until the power peak is found.

Due to the limitations of the current automation techniques discussedabove, there is a need for a knowledge based modeling process for theautomation of photonic systems in order to reach the potential of thehigh-capacity optical systems in which packaging and automation are keysto performance and cost.

SUMMARY OF THE INVENTION

Accordingly, it is an object of certain embodiments of the invention toprovide advanced automation as well as intelligent control to yield highperformance, low cost automation for packaging and assembly systems.

A knowledge based model is used to predict the best design, assemblyand/or packaging for a given application, along with a completelyautomated active optical feedback loop for ensuring an accurate andefficient automation of the design, packaging and/or assembly ofdevices.

In a first aspect of the invention, a system for the design, packagingand automated assembly of optoelectronic devices is disclosed. Thesystem includes an automated device configured for the manipulation andhandling of optoelectronic device components and a knowledge based modelderived from a set of parameters for optoelectronic devices. Theseparameters can comprise one or more of the following; alignment factors,type of assembly task, material type, geometry, dimensions, as well asoptical characteristics and features of the optoelectronic device and/orits components. There is also a database in which the knowledge basedmodel is stored for use by the system.

The system also includes a controller that controls the automateddevice. This controller is enabled to receive information from thedatabase. The controller is made up of an initial set point device, anda servo-feedback loop. The initial set point device uses the knowledgebased model for setting an initial set point. The servo-feedback loopbegins at the initial set point and controls the movement of theoptoelectronic components. A measuring device is used for takingmeasurements in the system. These measurements are used by theservo-feedback loop to adjust the movement of components in the system.

In a second aspect of the invention, a method for the design, packagingand automated assembly of optoelectronic devices is disclosed. Themethod includes the steps of providing an automated device configuredfor the manipulation of optoelectronic device components, determining aninitial set point using a knowledge-based model of the optoelectronicdevice, providing the initial set point to a servo-feedback loop,positioning the device to the initial set point, obtaining a measurementof the system with a measuring device and then using the measurement toadjust the position of one or more of the optoelectronic devicecomponents.

These and other aspects of the present invention will be apparent fromthe detailed descriptions of the invention, which follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a prior art method for performing automated optoelectronicpackaging.

FIG. 2 shows an overview of the system of the present invention

FIG. 3 shows an overview of another embodiment of the system of thepresent invention including a learning loop.

FIG. 4 shows an expanded breakdown of the knowledge-based method of thepresent invention.

FIG. 5 shows a diagram of simple model based control.

FIG. 6 shows a diagram of a learning model identification technique fora learning loop.

FIG. 7 shows a diagram of simulation of a learning loop technique fortwo unknowns.

FIG. 8 shows a setup of laser diode-aperture-fiber coupling.

FIG. 9 shows intensity cross-sections of plane wave propagation past anaperture 22.5, 56.25 and 225 μm.

FIG. 10( a) shows a contour diagram of the power coupled in an 8 μmfiber a wave front.

FIG. 10( b) shows an intensity contour of a wavefront propagated 22.5 μmpast a 30 μm aperture.

FIG. 11( a) shows a star coupler.

FIGS. 11( b)-11(c) show optical intensity contours three dimensions andtwo dimensions for the star coupler of FIG. 11( a).

FIG. 12 shows a comparison of the knowledge based model control methodof the present invention to a conventional alignment control method inboth two-dimensional and three-dimensional graphs.

FIG. 13 shows an iterative time-step comparison of the knowledge basedmodel control system of the present invention to the conventionalalignment control method employed in FIG. 8.

FIG. 14 shows fiber array alignment of the hill climbing algorithm andthe knowledge based model control loop.

FIG. 15 shows a model of the edge emitting laser coupled to a fiber.

FIG. 16 shows a chart showing the power distribution simulation at thefiber interface.

FIG. 17 (a) shows a diagram of the power contour.

FIG. 17( b) shows an intensity diagram showing the gradient ascentalgorithms for both the instant invention's model and a currently usedmodel.

FIG. 18 shows a diagram of the control process including thefeed-forward loop, and simulation results for the specificlaser-to-fiber example.

DETAILED DESCRIPTION 1. System Overview

The system and method of the present invention can be used in theautomation and assembly of a variety of optoelectronic devices such ascouplers, fiber optic couplers, fused biconical tapered couplers,switches, optical switches, wave-division multiplexers, filters,attenuators, polarizers, waveguides, sensors, fiber optic sensors,connectors, fiber optic connectors, pigtails, fiber optic pigtails,patch cords, fiber optic patch cords, transmitters, fiber optictransmitters, receivers, fiber optic receivers, amplifiers, an opticalamplifier, a fiber optic amplifier and other similar devices and/orcomponents. By using a knowledge based model packaging and assemblytechnique for the automation of photonic systems, the system of thepresent invention overcomes certain limitations of current photonicautomation systems. This knowledge based model automation techniquerequires accurate and efficient optical models. In that respect, thesystem preferably employs validated existing optical models and/or newadvanced models for complex devices and systems.

Knowledge based models provide a new paradigm for photonic automation.Previous device and process knowledge are exploited in on-line controlloops to optimize design, assembly and packaging of devices such asoptoelectronic devices. Not only will this decrease the cost of systemassembly and packaging, including alignment, this technique will employexisting capital equipment infrastructure (from semiconductor andindustrial automation) and increase the system performance in terms ofbit error rate (BER), signal-to-noise ratio (SNR), insertion loss,cross-talk, and coupling. As device and system designs become morecomplex, the advantages of this technique will be magnified.

FIG. 2 shows a general overview of one embodiment of a system inaccordance with the present invention. Controller 200 is comprised of aninitial set point device 202, and a servo-feed loop 204. Controller 200may typically be a CPU or other processing device that is connected tothe overall assembly system. Alternatively, controller 200 could becomprised of a plurality of processors connected to the system via theInternet, or by wireless connections.

The initial set point device 202 employs a knowledge-based model 210received from a database 208 in order to calculate the initial set pointX₀ used by the system. In one preferred embodiment, the knowledge-basedmode 210 is an optical power propagation model. However, other opticalwaveforms characteristics and features can could alternatively beemployed as the basis of the knowledge-based model 210, or as a portionof knowledge-based model 210, in order maximize the efficiency of thesystem. Other features or characteristics can be, for example, opticalintensity, optical phase, optical polarization and combinations thereof.The database 208 can be a CPU, or can be comprised of on-line storagedevices. The database 208 can be maintained at a separate locality fromthe assembly and be operated independently to supply the model 210 tovarious systems located at different locations. The controller 200 couldthen download the appropriate model 210 when needed. Alternatively thedatabase 208 could be stored on the same CPU as the controller 200.

The servo-feed back loop 204 uses the initial set point X₀ and at leastone measurement obtained from the measuring device 206 to operate theautomated device 212. The measurement obtained is typically that of anoptical feature or characteristic such as optical power, opticalintensity, optical phase or optical polarization, and can be measured ina variety of manners. Alternatively one or more other measurements couldbe made by the system and the set point could be established based uponthat measurement or a combination of different measurements. Ameasurement could be made of the optical intensity, optical phase,optical polarization, and combinations thereof. The automated device 212then operates to assemble the components of the device. An artisanfamiliar with the assembly and packaging of optoelectronic devices wouldbe familiar with the range and scope of suitable automated devices thatcan be used by the system for packaging and alignment of components.

FIG. 3, shows an alternative embodiment of a system in accordance withthe present invention. This alternative embodiment additionally includesa learning loop 314. Controller 300 comprises an initial set pointdevice 302 and a servo-feedback loop 304, however it also includeslearning loop 314. Learning loop 314 operates within the system to helpcontrol the automated device 312 in an improved manner. The controller300 will receive a knowledge-based model 310 from the database 308, andthe initial set point device 302 will use the knowledge-based model 310to provide the initial set point X₀. Learning loop 314 monitorsmeasurements taken by measuring device 306 and compares the values inorder to improve the set point determination based on theknowledge-based model 310 for future device assembly. This permits thesystem to make improvements to the knowledge-based model 310 based uponactual conditions occurring during the assembly process.

Although the operation of the system is detailed above, further detailwill be provided about the knowledge-based model 310 below. Theoverwhelming majority of currently deployed control loops are of thesimple feedback type, including Proportional (P), Proportional andIntegral (PI) or Proportional, Integral, and Derivative (PID). However,in addition to the feedback module, the Model Based Controller of thepresent invention includes a “feed-forward” element, which determinesthe initial set point. The feed-forward element is typically based upona priori knowledge regarding the process to be controlled. Such acontroller is denoted as a “Model Based Controller.” This family ofcontrollers includes: Model Reference Adaptive Control (MRAC), InternalModel Control (IMC), Model Predictive Control (MPC), and IntelligentControl such as Expert Control, Neurocontrol, and Fuzzy Logic Control.

FIG. 4 shows an expanded breakdown of the Model Based method employed bythe system of the present invention. As seen in the figure, there arethree main components of the control algorithm. The innermostservo-feedback loop 414, shown in FIG. 4, functions in a similar manneras the servo-feedback loop, servo-feedback loop 414 can be anoff-the-shelf servo feedback loop, typically a PID controller. Theservo-feedback loop 414 performs a gradient ascent 408 on the measuredoptical power P and attempts to converge on the local maximum opticalpower by comparing consecutive power readings P_(k) and P_(k−1) 412 atconfigurations x_(k) and x_(k−1). Once convergence is complete, thesystem proceeds to stop motion step 416. A gradient,(P_(k)−P_(k−1))/(x_(k)−x_(k−1)), is formed which guides the axis motionto the next configuration, x_(k+1):x _(k+1) =x _(k)+η((P _(k) −P _(k−1))/(x _(k) −x _(k−1)))where, η is the gradient ascent coefficient, which is the resolution ofthe step. Motion control step 410 performs the function of adjusting theoptical components based on the output of servo-feedback loop 414.However, in this case, the servo-feedback loop 414 is initialized with adifferent, more advanced set point X₀, as described below.

The feed-forward loop 405, denoted (B) in FIG. 4, provides theservo-feedback loop 414 with a “smart” initial set point to track. Therecan be a visual inspection and manual alignment 402, but use is alsomade of an optical power propagation model 403. The “smart” set point isselected by the initialization loop 404 on the basis of a properlyderived, optical power propagation model 403, which can be stored in adatabase or computed on-line. The optical power propagation model 403 isdevice and assembly task specific, that is, different devices withdifferent alignment and assembly tasks will possess unique powerdistribution functions. As new assembly tasks are submitted to thecontrol machinery (e.g., inputs to the feed-forward block), the model403 is activated and generates a new set point for the innerservo-feedback loop 414 to track and lock onto. This information is usedin the initialization loop 404. It is emphasized that X₀ generated bythe knowledge based model control method, in general, is different fromthe value of X₀ presently produced by the controller seen in FIG. 1.This new X₀ position forecasts a knowledge based model nominalconfiguration for maximum power transfer.

The knowledge based model control method can be derived from a set ofknown parameters for the optoelectronic device. For example, an opticalpower propagation model can be derived from set of one or more of thefollowing parameters for optoelectronic devices: alignment factors, typeof assembly task, material type, geometry, dimensions, design tradeoffsand the assembly apparatus. Therefore the system can take into account awide range of environmental factors as well as assembly and automationfactors in developing the control method. This information can be usedfor the optimization of the design of the automation system itselfand/or the components of the device. Assembly machinery can be adjustedas well.

In an alternative embodiment the system of the present invention furtherincludes a learning loop 418. Learning loop 418 is preferably theoutermost loop in order to provide opportunities for the system toimprove upon its knowledge-based model and adjust its accuracy on thebasis of “experienced evidence” or a mismatch between expected power andmeasured power at a specific axes configuration. The learning loop ispreferably only activated at a lower sampling frequency for specific andappropriate tasks. The data received by moving to set point X_(k) usingthe motion control loop and the measuring of the power P_(k) when X_(k)is reached is used by both the servo-feedback loop 414, and the learningloop 418. The learning loop will use the data in its learning algorithmand model parameter adjustment. The learning loop 418 can employmeasurements of optical power, alignment factors, material features,geometry and dimensions to adjust the knowledge based model. Thelearning loop can also use statistical quality control or field databased on experience of handling the device itself. A manufacturingdatabase can be updated based on information gathered during use of themachinery or maintenance, or observations made from other similarsystems employed in the automation of the packaging and assembly.Learning loop 418 is further explained with respect to FIGS. 5-7discussed below.

FIG. 5 shows the control algorithm block diagram of the feed forwardloop 405 and feedback loop 414. The feed forward loop 405 is defined bythe equation shown in FIG. 5. Feedback loop 414 and its definingtransfer function are also shown in FIG. 5. In the figure, the input isOptical Power Desired, P_(d), and the output is Optical Power Received,P_(r). The control plant is denoted as P, and the gain is noted asK_(p).

To effectively use the knowledge based model control, models of thecontrol plant, {circumflex over (P)}, and its inverse, {circumflex over(P)}⁻¹, must be determined for feed forward loop 405. If doneaccurately, feed-forward loop 405 can position the mechanics in thevicinity of the globally optimal configuration. If {circumflex over(P)}=P, where P is the actual behavior of the plant, perfect trackingcan be obtained, mathematically shown by multiplying the transferfunction of the two sub loops together:

$\begin{matrix}{\frac{P_{r}(s)}{P_{d}(s)} = {\frac{R(s)}{P_{d}(s)} \cdot \frac{P_{r}(s)}{R(s)}}} \\{= {{{\hat{P}}^{- 1}(Y)} + {K_{p}\frac{\hat{P}(X)}{{K_{p}{\hat{P}(X)}} + 1}}}} \\{= {{\hat{P}\left( {\hat{P}(X)} \right)} + {K_{p}\frac{\hat{P}(X)}{{K_{p}{\hat{P}(X)}} + 1}}}} \\{= \frac{1 + {K_{p}{\hat{P}(X)}}}{{K_{p}{\hat{P}(X)}} + 1}} \\{= 1}\end{matrix}$As seen, feed forward loop 405 relies on accurate models to determinethe set point. Therefore, the optical power propagation model isimportant in providing accurate models and learning loop 418 plays arole in providing an accurate power propagation model.

In the systems that are to be automated, the structure of the systemsand all of its parameter values are assumed to be known. Opticalpropagation models are then used to derive a mathematical representationof the system. Often, all of the required information is not available,or models could have inaccuracies. In such a case, a system model can beapproximately determined from experimental measurements of availableinputs and outputs. When the structure of the unknown system is known,but certain parameter values are unknown, system identification isreduced to a problem of parameter identification. This identificationtechnique is explored below with respect to optoelectronic automation.

An angular spectrum technique is used to model optical propagationbetween system components, including optical sources, fibers, devices,apertures, and detectors, due to its accuracy and computationalefficiency. The angular spectrum technique is an exact solution to theRayleigh-Sommerfeld formulation, a scalar modeling technique withoutnear and far field approximations. The technique is implemented byperforming a Fourier transform on the complex optical wavefronttransforming from the spatial domain to the frequency domain,multiplying these frequencies by a transfer function describing thepropagation medium, and returning to the spatial domain with the use ofan inverse Fourier transform. However, there are possible sources oferrors in the optical modeling technique. The first is the use of adiscrete Fourier transform in place of a continuous Fourier expression.Other errors can be found due to aliasing and sampling conditions.

To combat possible errors in the knowledge based model, learning loop418 is implemented to improve the models that are used for controlfunctions. The learning model identification for learning loop 418 isactivated at a lower sampling frequency for specific and appropriatetasks. This technique provides opportunities for the system to improveupon its power model and adjust its accuracy on the basis of“experienced evidence” or a mismatch between expected power and measuredpower at a specific axes configuration. Details of the learningidentification technique for learning loop 418 are explained below.

The system to be identified is described by {dot over (y)}=f(y,u,β),where y is the output, u is the input, and β is a vector of all of theunknown parameters. A mathematical model with the same form, withdifferent parameter values {circumflex over (β)} is used as a learningmodel, such that {circumflex over ({dot over (y)}=f(ŷ,u,{circumflex over(β)}). The output error vector, e, is defined as e=y−ŷ. The goal of thelearning loop is to manipulate {circumflex over (β)} such that theoutput is equal to zero. The implicit assumption is that e is determinedentirely by {circumflex over (β)} and is zero when {circumflex over(β)}=β. It follows that

$e = {{{e\left( \hat{\beta} \right)}\mspace{14mu}{and}\mspace{14mu}\overset{.}{e}} = {\left( \frac{\partial e}{\partial\hat{\beta}} \right){\overset{.}{\hat{\beta}}.}}}$The Lyapunov function, v(e), is used to determine the stability of thesystem. In this case, v(e) is selected as a positive definite functionof e (that is, if v(0)=0 then v(e)>0 for all e≠0) and is defined as

${{v(e)} = {\frac{1}{2}e^{T}Q\; e}},$where Q is a symmetric matrix. Therefore, the derivative of the functionis

${\overset{.}{v}(e)} = {e^{T}Q\frac{\partial e}{\partial\hat{\beta}}{\overset{.}{\hat{\beta}}.}}$If {dot over (v)}(e) could be made negative definite (that is, if v(0)=0then v(e)<0 for all e≠0) by properly choosing {circumflex over ({dotover (β)}, then e would approach zero asymptotically. Selecting

${\overset{.}{\hat{\beta}} = {{- {ɛ\left( \frac{\partial e}{\partial\hat{\beta}} \right)}^{T}}Q\; e}},$with ε as a positive scalar constant, gives a negative semi definite(that is, v(e)≦0 for all e≠0) expression

${\overset{.}{v}(e)} = {{- ɛ}\; e^{T}{Q\left( \frac{\partial e}{\partial\hat{\beta}} \right)}\left( \frac{\partial e}{\partial\hat{\beta}} \right)^{T}Q\;{e.}}$Even though not negative definite, this learning model technique forlearning loop 418 is capable of providing system identification in manycases.

Before this can be implemented, the sensitivity matrix,

$\frac{\partial e}{\partial\hat{\beta}},$must be computed. y does not depend on {circumflex over (β)}, therefore,

$\frac{\partial e}{\partial\beta} = {{- \frac{\partial\hat{y}}{\partial\hat{\beta}}} \cong {S.}}$Since the initial conditions for the model ŷ(0) can be selectedindependently of {circumflex over (β)}(0), the initial condition for thesensitivity matrix S is S(0)=[0]. The learning model adjustment schemeconsists of assuming initial values for {circumflex over (β)}(0),adjoining the sensitivity equations to the model equations and using{circumflex over ({dot over (β)}=−εS^(T)Qe. The learning modelidentification technique for learning loop 418 can be visualized interms of the control diagram shown in FIG. 6.

As in all gradient adjustment schemes, the parameter ε must be properlyselected. If ε is too large, the schemes will diverge, and if ε is toosmall, then {circumflex over (β)} will approach β very slowly. Generalconditions under which this technique converges are difficult todetermine analytically. Selection of a suitable ε and the weightingmatrix Q are determined by a trial and error process.

To show an example of this learning identification theory for learningloop 418, a system with two unknown variables having an input-outputdifferential equation ÿ+a{dot over (y)}=Ku is discussed below. In thisexample, a and K are unknown (i.e., need to be learned), and thevariables u, y, and {dot over (y)} can be measured. Using the learningidentification theory discussed above, the equations necessary toimplement the learning model identification scheme are y=x₁ and {dotover (y)}=x₂, resulting in the derivate of the state

${{variable}\mspace{14mu}\overset{.}{x}} = {{\begin{bmatrix}0 & 1 \\0 & {- a}\end{bmatrix}x} + {\begin{bmatrix}0 \\K\end{bmatrix}{u.}}}$In the system, an estimated model of

$\overset{.}{\hat{x}} = {{\begin{bmatrix}0 & 1 \\0 & {- \hat{a}}\end{bmatrix}\hat{x}} + {\begin{bmatrix}0 \\\hat{K}\end{bmatrix}\hat{u}}}$is used; therefore, the error is defined as e=x−{circumflex over (x)}.The necessary sensitivity coefficients are contained in

${S = \begin{bmatrix}\frac{\partial e_{1}}{\partial\hat{a}} & \frac{\partial e_{1}}{\partial\hat{K}} \\\frac{\partial e_{2}}{\partial\hat{a}} & \frac{\partial e_{2}}{\partial\hat{K}}\end{bmatrix}},$where

${e = \begin{bmatrix}{y - \hat{y}} & {\overset{.}{y} - \hat{\overset{.}{y}}}\end{bmatrix}^{T}},{\frac{\partial e}{\partial\hat{a}} = {- \frac{\partial\hat{x}}{\partial\hat{a}}}},{and}$$\frac{\partial e}{\partial\hat{K}} = {- {\frac{\partial\hat{x}}{\partial\hat{K}}.}}$The initial conditions on all four sensitivity equations are zero. FIG.7 gives the simulation diagram for this method.

Using a knowledge based model control technique provides many advantagesover current automation techniques. For example, the technique cansupport the packaging of systems not emitting optical power in an idealuni-mode power distribution. Therefore, if the optical powerdistribution has many peaks and valleys, using a knowledge-based modelenables prior knowledge of which peak will nominally contain the mostoptical power. From the position of the power peak, optimal alignmentcan be obtained, as the control loop avoids finding and being positionedin local power maximums. Unavoidable errors, such as manufacturingerrors and misalignments, will be partially corrected with a PIDfeedback loop, found in addition to the feed-forward loop. An additionaladvantage of the technique, when compared to today's standards, is thetime that the automation control loop takes to track the peak powerposition. It can greatly decrease this time with the feed-forward blockof the algorithm. Using advanced simulations, an initial position thatis close to the optimal position can be found. Therefore the system doesnot have to search the complete optical field space. This reduces therequired field of view and required resolution, which can lower the costof the automation sensors, software, and hardware. Also, as the numberof packages to be assembled increases, the packaging time of anindividual device is critical. This time directly effects the packingtime of the entire lot of devices, which is an important cost factor forlarge manufacturing runs.

For example, having prior knowledge of how tilts of the fiber orwaveguide affect the performance of the system is important. Tilts arethe most challenging aspect of alignment using the current methods. Touse gradient ascent to position in the x and y directions is fairlystraightforward for a uni-mode optical power distribution. However, whenadding the complexity of tilts into the alignment, the control loopdramatically slows down as the number of parameters required to optimizethe alignment position increases. With the knowledge based model controlalgorithm, the system can reduce the costly time of optimizing tilted,and more generally, multi-axis systems.

2. Optical Modeling Techniques

a. Rayleigh-Sommerfield Technique

As part of the knowledge based model control system for the automationof photonic devices, there is a need to perform accurate, yet efficient,optical modeling for the feed-forward portion of the control algorithm.In this section, an optical modeling and simulation technique that isused in the system of the present invention is described in detail.

When optical wavefronts interact with the small feature sizes ofmicro-systems, many of the common optical propagation modelingtechniques become invalid, and full vector solutions to Maxwell'sequations are required for accurate simulation. However, these accuratesolutions are computationally intensive, making interactive simulationbetween the control loop and the optical modeling tool almostimpossible. To reduce the computational resources of modeling theoptical wavefront in free-space by the vector solutions, a scalarrepresentation can be used. For example, the Rayleigh-Sommerfeldformulation can be employed. The Rayleigh-Sommerfeld formulation isderived from the wave equation for the propagation of light infree-space from the aperture plane (ξ,η,0) to a parallel observationplane (x,y,z). The Rayleigh-Sommerfeld formulation is mathematicallyshown below:

${U\left( {x,y,z} \right)} = {\left( {z/{j\lambda}} \right)\underset{\Sigma}{\int\int}{U\left( {\xi,\eta,0} \right)}{\left( {\exp\left( {j\; k\; r} \right)} \right)/r^{2}}{\partial\xi}{\partial\eta}}$where, r=(z²+(x−η)²+(y−ξ)²)^(1/2), Σ is the area of the aperture, and zis the distance that the light is propagated from an aperture plane(z=0) to the observation plane. The formulation is valid as long as boththe propagation distance and the aperture size are greater than thewavelength of light. These restrictions are based on the boundaryconditions of the Rayleigh-Sommerfeld formulation, and the fact that theelectric and magnetic fields cannot be treated independently at theboundaries of the aperture. To compute the complex wavefront at theobservation plane, each plane is discretized into an N×N mesh. Using adirect integration technique, the computational order of theRayleigh-Sommerfeld formulation is O(N⁴). In the interest of reducingthe computational load of using a full scalar technique, theRayleigh-Sommerfeld formulation has been recast using an angularspectrum technique.

b. Angular Spectrum Technique

As an alternative to direct integration over the surface of thewavefront, the Rayleigh-Sommerfeld formulation can also be solved usinga technique that is similar to solving linear, space invariant systems.Re-examining the Rayleigh-Sommerfeld formulation, it can be seen thatthe equation is in the form of a convolution between the complexwavefront and the propagation through free space. The Fourier transformof the complex optical wavefront results in a set of plane wavestraveling in different directions away from the surface. Each plane waveis identified by the components of the angular spectrum. At theobservation plane, the plane waves are summed together by performing aninverse Fourier transform, resulting in the propagated complex opticalwavefront at the observation plane.

To solve the Rayleigh-Sommerfeld formulation with the angular spectrumtechnique, the complex wavefront at the aperture plane is firstexamined. The wave function U(x,y,z) has a 2D Fourier transform,A(v_(x),v_(y),0), in terms of angular frequencies, v_(x) and v_(y).A(v _(x) ,v _(y),0)=∫∫U(x,y,0)exp[−j2π(v _(x) ,x+v _(y) y)]∂x∂y, where v_(x)=sin θ_(x)/λ and, v _(y)=sin θ_(y)/λFrom the equation, the plane waves are defined byexp[−j2π(v_(x),x+v_(y)y)] and the spatial frequencies define thedirectional cosines, sin (θ_(x)) and sin (θ_(y)), of the plane wavespropagating from the origin of the aperture plane's coordinate system.

The free-space transfer function in the frequency domain has beencomputed by satisfying the Helmholtz equation with the propagatedcomplex wave function, U(x,y,z):A(v _(x) ,v _(y) ,z)=A(v _(x) ,v _(y),0)exp{jz2π(1/λ² −v _(x) ² −v _(y)²)^(1/2)}This describes the phase difference that each of the plane waves,differentiated by the spatial frequencies, experiences due to thepropagation between the parallel planes. Therefore, the wave functionafter propagation can be transformed back into the spatial domain withthe following inverse Fourier transform:U(x,y,z)=∫∫A(v _(x) ,v _(y),0)exp{jz2π(1/λ² −v _(x) ² −v _(y)²)^(1/2)}exp[j2π(v _(x) ,x+v _(y) y)]∂v _(x) ∂v _(y)The advantage of using the angular spectrum to model light propagationis that the method is based on the Fourier transform. The computationalorder of the FFT for a 2D input is O(N² log₂ N), which allows forsimulation to be performed on-line in the knowledge based model controlloop.

The techniques discussed above are not exclusive of the techniquesavailable, and other methods of developing knowledge-based models couldbe used within the scope of the present invention. In the preferredembodiment Rayleigh Sommerfeld formulation or an angular spectrumsolution to the Rayleigh Sommerfeld formulation is used. However, Rayanalysis or Gaussian analysis (Salech, B. E. A. and Teich, M. C.,Fundamentals of Photonics (see Wiley, New York), 1991, or Far(Fraunhofer) Field analysis or Near (Fresenal) Field analysis (seeHecht, E., Optics, Second Edition (Addison-Wesley Publishing Company,1987), or these methods can be used as a part of the knowledge basedmodel in the development of the automation. Also vector solutions toMaxwell's equations (see Scarmozzino, R., Osgood, R. M., Jr.,“Comparison of finite-difference and Fourier-trans-form solutions of theparabolic wave equation with emphasis on integrated-opticsapplications,” Journal Optical Society of America A, Vol. 8, No 5, May1991, pp. 725-731) can be used.

3. Examples

To highlight some of the advantages of the knowledge based model, hereare some examples comparing the knowledge based model to current,state-of-the-art alignment algorithms. Examined below is the coupling ofa wavefront into a fiber in the near field and a more complex systemcoupling the output of a diffractive element into a fiber array. First adiscussion of the equipment setup is presented.

Equipment Set Up

A diagram of a sample test bed 800 is shown in FIG. 8. The sample testbed 800 is illustrative of a possible bed that may be used incombination with Examples 1-4 described in detail below. FIG. 8 shows adiagram of optical table 810, which is an XY table, an X servo motor814, a Y servo motor 812, an X encoder 816, a Y encoder 818, amplifiers(not shown), DAQ (data Acquisition) 824, an optical power meter 826, alaser diode driver 828, a pigtailed laser diode 830, and a DSP-basedmotion controller board 820 from Precision Microdynamics Inc.™ TheMC8000 motion control board 820 uses a 32-bit floating point DSP thatperforms path planning, feedback regulation and other real timecomputations, freeing the host PC for process application. The cardsupports data rates with the host PC as high as 7.2 Mbytes/s.

Servo motors, 812, 814, are manufactured by BEI™, and are typical“inside-out” brushless DC (BLDC) motors which provide greater outputpower, higher operating speeds and cleaner, quieter operation thanbrush-type counterparts. Motors 812, 814 are ideal for sterileenvironments, since there are no brushes and no particulate isdischarged. Because of their inherent reliability and long-term servicelife, BLDC motors can significantly contribute to lower overall cost ofoperation and maintenance. The amplifier (not shown), made by AMC™, is aPulse Width Modulated (PWM) trans-conductance amplifier with a gain of2.85 Amp/Volt and supply voltage requirement of 70V. The Heidenhain™ LIP403A encoders, 816, 818, have a grating of 2 μm and maximum speed of 6m/min, with a sinusoidal output.

The motion control card receives position commands issued by the PCsoftware from feed forward loop 405. Computer 822 calculates a series ofpositions for each axis along the desired path at the desired speed setby the feed forward loop 405. The motion control card adjusts thesignals to the servo amplifiers accordingly, such that servomotors 812,814 follow the desired path. To make sure that the desired path isfollowed and the loop is closed, the motion control card repeatedlychecks the actual position of the machine's axes obtained from encoders816, 818 against the commanded position and makes adjustments to keepthe difference as small as possible.

The complete system set-up to couple an optical fiber to a laser sourceis shown in FIG. 8. Optically, 680, 1330, and 1550 nm pig-tailed laserdiodes 830 are coupled to single mode fibers, a 501 Newport Driver, andan 1830-C Newport Fiber Receiver are used. The receiver is GBIPinterfaced to the computer control. Pigtailed laser diode 830 isattached to the non-moving test bed structure, while the receiver fiberis attached to the controlled optical table 810. The optical powersensor reading is sent to the computer control, which controls thesystem for position measurement to attach at the point of maximum power.

Example 1 Near Field Alignment

In this example, the coupling of a plane wave propagating through a 30μm square aperture and an 8 μm fiber in the near field is examined.Under these circumstances, the system of the present invention providesbetter performance than the current automation method. As discussedabove, current alignment automation techniques determine an initial setpoint through the visualization of the position of the fiber relative tothe aperture, and alignment of the geometrical optical axis with centerof the fiber core. From this set point, the gradient ascent algorithm isperformed to find the positional alignment that provides the maximumpower coupled into the fiber. For a square aperture system in which theoptical wavefront has propagated into the far field, the wavefront powerdistribution will be a sine function in the x and y directions, with apower maximum at the geometric center of the system. Therefore, thevisualization set point would lead to an attachment for the coupling ofmaximum power into the fiber.

However, if the optical wavefront has only propagated into the nearfield, the wavefront appears much different than that of the far fieldpattern, and attachment at the optical geometric axis will lead to apoor system performance. Demonstrating the difference between the nearfield and the far field, FIG. 6 shows a cross-section of the intensitydistribution of a plane wave propagating past a square aperture 22.5,56.25, and 225 μm. As the wavefront propagates further past theaperture, it starts to move from the near field to the far field as a“Gaussian-like” shape begins to appear in the center of the wavefront.At the top of FIG. 6, a diagram of the plane wave propagating through anaperture is included.

Using the knowledge based model to determine the positional alignmentfor the maximum power coupled into the 8 μm fiber at a distance of 22.5μm past the aperture, the entire system is simulated to predict the bestfeed-forward set point for the control algorithm. The simulation isperformed using the angular spectrum technique for solving theRayleigh-Summerfeld formulation discussed above, since the outputintensity distribution and a distribution of the power coupled into thefiber are determined. From the power distribution, the position of themaximum power value is scanned for, which becomes the feed-forward setpoint in the knowledge based model control algorithm. The graph of thepower distribution into the fiber is seen in FIG. 10( a), and thefeed-forward set-point is approximately (7,7) μm. The intensity contourof a wavefront propagated 22.5 μm past a 30 μm square aperture is shownin FIG. 10( b).

In FIG. 10( a), the coupling of the fiber using the currentstate-of-the-art technique and the knowledge based model controlalgorithm is also compared. The classic technique starts at a positionclose to the center of the geometrical optical axis and uses thegradient ascent algorithm, which stops the alignment loop at a localpower maximum, denoted by the “X” in the figure. In contrast, theknowledge based model control technique starts at the feed-forwardposition (in this example, it actually starts off of the set point by acouple of microns to simulate possible mechanical and systemmisalignments) and uses a gradient ascent algorithm to find the globalmaximum of power coupled into the fiber, denoted by the “O” in thefigure.

The paths of the gradient ascent algorithms for both the instantinvention's method and the classical method are included on theintensity diagram. In this example, an increase in system performance ofapproximately 18% is achieved when using the knowledge based model. Theknowledge based model peak does not just find the maximum intensitypeak, it examines the entire power distribution, and finds the bestsystem performance.

Example 2 Fiber Array Automation

In this example, an automated process for aligning and attaching a fiberarray to a star coupler is examined. The star coupler is shown in FIG.11( a). An array of 8 fibers is coupled to the waveguide outputs of thestar coupler. The spacing between the waveguides and the fibers in thefiber array are matched to increase system performance. To make thesystem more realistic, the star coupler input is excited with an opticalpulse, with a tilt of 2 degrees, which is a tilt misalignment that canbe reasonably expected to occur during use of current semi-automaticassembly processes. With the use of simulation the output wavefront thatis expected from the star coupler can be determined. The 3D and 2Dcross-section intensity contours, simulated in RSoft's BeamProp, areshown at the edge of the output of the star coupler. These results arealso seen in FIGS. 11( b)-11(c).

As in Example 1 above, the current industry standard is first performedfor alignment and packaging automation for comparison with the knowledgebased model. This is achieved by performing the gradient ascent, or“hill climbing”, technique to find the peak power position of the firstfiber, as previously described above. The first possible error using thehill climbing technique is that the positioning of the first fiber canoccur at a local maximum. This is shown in FIG. 12, as both a twodimensional intensity contour and a three dimensional figure. Thehill-climbing algorithm is started at a position, denoted by the circlein FIG. 12, which is roughly half the array pitch spacing, in both the xand y direction, and runs until a maximum is determined. However, asdenoted by the line with the “+” symbol in FIG. 12, the hill-climbingtechnique “zigzags” and stops at a local maxima (denoted by the square)before the global peak power for the first fiber. The peak intensity atthis local position is 0.0502 (AU).

In contrast, the knowledge based model discussed above is shown by thepath marked with the “*” symbol in FIG. 12. From the device modelsimulation, the “feed-forward” control block determines where themaximum power peak will occur and sets this initial position in thecontrol loop. In this example, the initial point is positioned roughly5% away from the maximum value, to simulate the possibility of opticalmodeling errors, equipment misalignments, and/or errors due tomanufacturing tolerances. The technique quickly finds the maximum powerfor coupling to the first fiber in the array, which is denoted by a starin FIG. 12. The peak optical intensity found at this peak is 0.2376(AU), which is an increase of over 370% over the result of employing thecurrent method as discussed above.

Besides finding the global maximum power peak, the technique is moreefficient when compared to the currently used alignment algorithm. Evenin this simple example, the number of time-steps, or steps that themotors had to take to get to the maximum power position, is much lessfor the knowledge based model (˜8 steps) than the standard hill-climbingtechnique (˜23 steps), as seen in FIG. 13, which got caught in a localminimum and did not even reach the peak power position. The time-steps,in essence, reflect the speed of the automation process of the presentinvention.

Example 3

In Example 3, the algorithm for improving the performance of the entiresystem is described. The total power captured in the fiber array isexamined. A common technique aligns a fiber array by determining theposition for the maximum optical power in the first fiber, as showed inthe example above. The remainder of the fiber array is then rotatedaround this position, until the maximum power is captured in the lastfiber of the array. It is then assumed that the rest of the fiber arrayis aligned.

In this example, it is shown that in aligning a fiber array using thistechnique, the overall performance of the system is not considered. Incontrast, the knowledge based model control loop of the presentinvention can take the performance of all of the fibers intoconsideration and thereby provide an increase in total systemperformance.

In this example, the total power of the fiber array is calculated bysumming the optical intensity at each of the center fiber positions inthe array. In the case of the hill-climbing technique, if the peakposition of the first fiber is caught in a local maximum, as seen inFIG. 12, the total power of all 8 fibers is calculated to be 0.1959(AU). If the hill-climbing algorithm is allowed the benefit of the doubtthat the true optical peak for the first fiber can be found at theglobal maximum value, the total power calculated for the fiber array is1.5296 (AU).

In contrast, with the knowledge based model approach, the entire opticalfield space can be examined by taking a plurality of measurements ofoptical power at different locations, and the position in which acertain alignment will achieve a maximum performance for the entiresystem can be determined. The total power is calculated for each case,and the optimal position of the fiber array is chosen at the point wherethe alignment gives the best performance for the entire system. In thiscase, it was found to be a maximum power of 2.0380 (AU), at a positionoffset from the first fiber center by about 3 μm in the x direction.Comparing the instant invention's technique verses the standardhill-climbing technique, an improvement of over 33% is shown when thehill-climbing uses the peak maximum of the first fiber, and over 940%when the hill climbing method gets caught in the local maximum.

In FIG. 14, the positioning of each fiber in the array is shown usingboth the classical technique (centered at the peak power of the firstfiber) and the knowledge based model technique. It can be seen that theknowledge based model control loop (denoted by the “+” shape) is closerto more array peaks than the hill-climbing technique (denoted by the “o”shape).

Example 4 Coupling of an Edge-Emitting Laser Diode to an Optical Fiber

This example highlights some of the advantages of the knowledge basedmodel. In this example, coupling of an edge-emitting laser diode to anoptical fiber is shown. This example illustrates one of the mostcommonly packaged devices using conventional optical automationprocesses.

A model of device 900 used is seen in FIG. 15. In this example, a GaAslaser diode 901 is flip-chip bonded onto a silicon bench 903, containingthe electrical drivers for the laser, along with a fabricated V-groove904 for placement of fiber 902. The V-groove 904 provides“self-alignment” for the fiber 902, however, within the V-groove 904,the positioning of fiber 902 is critical to the final performance ofdevice 900. The arrows 905 represent the 6 degrees of freedom (in both2D and 3D) in which fiber 902 needs to be aligned.

In this example, the laser diode emits a broad Gaussian beam, whichpropagates through a 20×20 μm square aperture to a fiber with a 4 μmcore. The aperture is used in this example, to ensure that the powerdistribution is not a simple uni-mode. The distance of propagationbetween laser-diode and fiber is only 10 μm, therefore the light haspropagated only into the near-field and its 2D intensity pattern in anobservation plane at the fiber shows diffractive effects, as seen inFIG. 16. This result is determined from the angular spectrum simulation.

In order to compare a conventional method and the method used in thisexample, the control loop is first analyzed with the conventionalmethod. The conventional automation process determines an initial setpoint in the V-groove 904 through the visualization of the fiber 902 tothe aperture, aligning the geometrical optical axis with the center ofthe fiber core (at a location of 20 μm in FIG. 16). From this set point,the gradient ascent algorithm is performed to find the positionalignment for maximum power coupled into the fiber 902.

In contrast, the knowledge based model determines the positionalalignment for the maximum power coupled into the fiber 902. Therefore,the knowledge based model starts by simulating the entire system topredict the best “feed-forward” set point. The simulation is performedusing the angular spectrum technique, as the output intensitydistribution and a distribution of the power coupled into the fiber 902are determined. In this example, the feed-forward set point is definedwith the simulated position of the maximum power (the area underneaththe intensity curve) captured in the 4 μm fiber, seen in FIG. 17( a).The position is found at (13.8, 13.8 μm).

In FIG. 17( b), the fiber is coupled using the conventional techniqueand the knowledge based model is compared to the conventional technique.The conventional technique starts at a position close to the center ofthe geometrical optical axis (20,20 μm) and uses the gradient ascentalgorithm, which stops the alignment loop at a local maximum power,denoted by the “X” in the figure. In contrast, the knowledge based modelstarts at the feed-forward position (13.8, 13.8 μm) and uses a gradientascent algorithm to find the global maximum power coupled into thefiber, denoted by the “O” in the figure (actually, in this example, thealgorithm starts off of the set point by a couple of microns to simulatepossible mechanical and system misalignments). The paths of the gradientascent algorithms for both the knowledge based model and theconventional method are included on the intensity diagram in FIG. 17(b). In this example, an increase of approximately 18% is achieved whenusing the knowledge based model, as compared to the conventionalautomation technique.

Using the same laser diode-to-fiber coupling shown in FIG. 15, acomplete simulation of the proposed automation control process is shownin FIG. 18. Again, an on-line simulation is performed at the point ofattachment. For these on-line simulations, the maximum throughput powerfor the ideal positional alignment of the fiber 902 is determined. Thisis used as a target or tracking parameter. In this simulation, theangular spectrum optical modeling technique is used, and determines apeak intensity value of 1.41 (AU). The inverse model is calculated withthe PWL deconstruction as presented in the previous section. Using asimple 1/(s+1) motor dynamic, the entire control loop is simulated inMATLAB's™ Simulink. Also included in FIG. 18, are simulation results, interms of optical power received vs. time and motor position vs. time.Note for these control parameters, the position of the motor settles ata distance of 12.6 μm, which tracks the goal intensity value of 1.41, inapproximately 7 seconds.

From the above examples, the effectiveness of the knowledge based modelof the present invention can be seen. It is to be understood that eventhough numerous characteristics and advantages of the present inventionhave been set forth in the foregoing description, together with detailsof the method, the disclosure is illustrative only, and changes may bemade within the principles of the invention to the full extent indicatedby the broad general meaning of the terms in which the appended claimsare expressed.

1. A system for the automation of one or more of the design, assemblyand packaging of optoelectronic devices comprising: an automatedmanipulation device configured for the manipulation of an optoelectronicdevice component; a knowledge based model derived using a formula and aset of one or more parameters for said optoelectronic device; whereinthe formula is selected from one or more of a Rayleigh-Sommerfeldformulation, an angular spectrum solution to a Rayleigh-Sommerfeldformulation, a Ray formulation, a Gaussian formulation, a FraunhoferField Formulation, a Fresenel Field formulation, and vector solutions toMaxwell's equations; a database for storing said knowledge based model;a measuring device for taking a measurement of one or more parameters ofthe optoelectronic device component; a controller for managing saidautomated manipulation device, said controller enabled to receiveinformation from said database; wherein said controller comprises aninitial set point device which utilizes said knowledge based model todetermine an initial set point for said automated manipulation device,and a servo-feedback loop which utilizes said measurement of one or moreparameters of the optoelectronic device component to determine amanipulation of the optoelectronic device component; an initial setpoint derived from the knowledge based model and a learning loop whichmakes adjustments to said knowledge based model based on actualexperience in one or more of the design, assembly, packaging, use andmaintenance of said optoelectronic device.
 2. The system according toclaim 1, wherein said one or more parameters comprises one or moreparameters selected from the group consisting of optical waveformcharacteristics and optical waveform features.
 3. The system accordingto claim 2, wherein the knowledge based model employs one or more ofoptical power, optical intensity, optical phase and opticalpolarization.
 4. The system according to claim 3, wherein the knowledgebased model is derived using a formula selected from the groupconsisting of a Rayleigh Sommerfeld formulation and an angular spectrumsolution to a Rayleigh Sommerfeld formulation.
 5. The system accordingto claim 1, wherein said set of parameters comprises one or moreparameters selected from the group consisting of optical waveformcharacteristics and optical waveform features.
 6. The system accordingto claim 5, wherein the knowledge based model employs one or more ofoptical power, optical intensity, optical phase and opticalpolarization.
 7. The system according to claim 6, wherein the knowledgebased model is derived using a formula selected from the groupconsisting of a Rayleigh Sommerfeld formulation and an angular spectrumsolution to a Rayleigh Sommerfeld formulation.
 8. The system as claimedin claim 7, wherein at least one said measurement is employed by saidlearning loop in the adjustment of said knowledge based model.
 9. Anautomated method for one or more of the assembly and packaging ofoptoelectronic devices comprising the steps of: (a) providing anautomated manipulation device configured for the manipulation of anoptoelectronic device component; (b) determining an initial set pointfor said automated manipulation device from a knowledge based model;wherein the knowledge based model is derived using one or more of aRayleigh-Sommerfeld formulation, an angular spectrum solution to aRayleigh-Sommerfeld formulation, a Ray formulation, a Gaussianformulation, a Fraunhofer Field Formulation, a Fresenel Fieldformulation, and vector solutions to Maxwell's equations; (c)positioning said automated manipulation device at said set point; (d)measuring at least one parameter of the optoelectronic device component;(e) adjusting the position of said automated manipulation device basedon said measurement; and (f) repeating steps (d)-(e) until saidoptoelectronic device is assembled, packaged or assembled and packagedand a learning loop which makes adjustments to said knowledge basedmodel based on actual experience in one or more of the design, assembly,packaging, use and maintenance of said optoelectronic device.
 10. Themethod according to claim 9, wherein said at least one parametercomprises one or more parameters selected from the group consisting ofoptical waveform characteristics and optical waveform features.
 11. Themethod according to claim 10, wherein the knowledge based model employsone or more of optical power, optical intensity, optical phase andoptical polarization.
 12. The method according to claim 11, wherein theknowledge based model is derived using a formula selected from the groupconsisting of a Rayleigh Sommerfeld formulation and an angular spectrumsolution to a Rayleigh Sommerfeld formulation.
 13. The method accordingto claim 9, wherein said set of parameters comprises one or moreparameters selected from the group consisting of optical waveformcharacteristics and optical waveform features.
 14. The method accordingto claim 9, wherein the knowledge based model employs one or more ofoptical power, optical intensity, optical phase and opticalpolarization.